Summary of the paper

Title Reliable Baselines for Sentiment Analysis in Resource-Limited Languages: The Serbian Movie Review Dataset
Authors Vuk Batanović, Boško Nikolić and Milan Milosavljević
Abstract Collecting data for sentiment analysis in resource-limited languages carries a significant risk of sample selection bias, since the small quantities of available data are most likely not representative of the whole population. Ignoring this bias leads to less robust machine learning classifiers and less reliable evaluation results. In this paper we present a dataset balancing algorithm that minimizes the sample selection bias by eliminating irrelevant systematic differences between the sentiment classes. We prove its superiority over the random sampling method and we use it to create the Serbian movie review dataset ― SerbMR ― the first balanced and topically uniform sentiment analysis dataset in Serbian. In addition, we propose an incremental way of finding the optimal combination of simple text processing options and machine learning features for sentiment classification. Several popular classifiers are used in conjunction with this evaluation approach in order to establish strong but reliable baselines for sentiment analysis in Serbian.
Topics Opinion Mining / Sentiment Analysis, Document Classification, Text categorisation, Corpus (Creation, Annotation, etc.)
Full paper Reliable Baselines for Sentiment Analysis in Resource-Limited Languages: The Serbian Movie Review Dataset
Bibtex @InProceedings{BATANOVI16.284,
  author = {Vuk Batanović and Boško Nikolić and Milan Milosavljević},
  title = {Reliable Baselines for Sentiment Analysis in Resource-Limited Languages: The Serbian Movie Review Dataset},
  booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)},
  year = {2016},
  month = {may},
  date = {23-28},
  location = {Portoro┼ż, Slovenia},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Sara Goggi and Marko Grobelnik and Bente Maegaard and Joseph Mariani and Helene Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
  publisher = {European Language Resources Association (ELRA)},
  address = {Paris, France},
  isbn = {978-2-9517408-9-1},
  language = {english}
 }
Powered by ELDA © 2016 ELDA/ELRA